Analysis of PANoptosis-related ceRNA network reveals lncRNA MIR17HG involved in osteogenic differentiation inhibition impaired by tumor necrosis factor-α

Background Inflammatory cytokines such as Interleukin 1β(IL1β), IL6,Tumor Necrosis Factor-α (TNF-α) can inhibit osteoblast differentiation and induce osteoblast apoptosis. PANoptosis, a newly identified type of programmed cell death (PCD), may be influenced by long noncoding RNA (lncRNAs) which play important roles in regulating inflammation. However, the potential role of lncRNAs in inflammation and PANoptosis during osteogenic differentiation remains unclear. This study aimed to investigate the regulatory functions of lncRNAs in inflammation and apoptosis during osteogenic differentiation. Methods and results High-throughput sequencing was used to identify differentially expressed genes involved in osteoblast differentiation under inflammatory conditions. Two lncRNAs associated with inflammation and PANoptosis during osteogenic differentiation were identified from sequencing data and Gene Expression Omnibus (GEO) databases. Their functionalities were analyzed using diverse bioinformatics methodologies, resulting in the construction of the lncRNA-miRNA-mRNA network. Among these, lncRNA (MIR17HG) showed a high correlation with PANoptosis. Bibliometric methods were employed to collect literature data on PANoptosis, and its components were inferred. PCR and Western Blotting experiments confirmed that lncRNA MIR17HG is related to PANoptosis in osteoblasts during inflammation. Conclusions Our data suggest that TNF-α-induced inhibition of osteogenic differentiation and PANoptosis in MC3T3-E1 osteoblasts is associated with MIR17HG. These findings highlight the critical role of MIR17HG in the interplay between inflammation, PANoptosis, and osteogenic differentiation, suggesting potential therapeutic targets for conditions involving impaired bone formation and inflammatory responses. Supplementary Information The online version contains supplementary material available at 10.1007/s11033-024-09810-0.


Introduction
Inflammatory cytokines play pivotal roles in the pathogenesis of bone infections.Tumor Necrosis Factor-alpha (TNFα) is a key pro-inflammatory cytokine and a catabolic factor in the inflammatory response to disease [1,2].Persistent inflammation significantly affects bone regeneration as TNF-α binds to different receptors and initiates signal transduction pathways [3,4], leading to various cellular responses including cell survival, differentiation, and proliferation.Once the signal transduction pathways are elucidated, strategies can be adopted to reduce the inhibitory effects of inflammation on tissue regeneration [5,6].Among the various signal transducer and activator of transcription (STATs), STAT3 is particularly important in mediating inflammation and bone formation [7][8][9].Our previous study showed that under TNF-α stimulation, Elongator acetyltransferase complex subunit 2 (ELP2) inhibits osteogenic differentiation through STAT3 activation [10].
In an inflammatory environment, the differentiation capacity of osteoblasts is inhibited, and osteoblast death is exacerbated [11,12].Osteoblasts often undergo apoptosis in response to inflammation, but other forms of programmed cell death (PCD) mechanisms such as pyroptosis and necroptosis also play crucial roles [13,14].Although each of these three PCD pathways has been previously regarded as unique based on the identification of important promoters, effectors, and executors, there is mounting evidence that they interact extensively [15,16].Based on these findings, we developed the concept of panoptosis.It is an inflammatory PCD route controlled by the PANoptosome complex, which possesses important characteristics of pyroptosis, apoptosis, and/or necroptosis.These characteristics could not be explained by any of the PCD pathways alone [17][18][19].
The miR-17-92 cluster, encoded by the host gene MIR17HG, is involved in various biological activities, including tumor development, neurodevelopment, and immune regulation [20][21][22].This cluster has a regulatory role in inflammation [23], with studies showing that inhibition of MIR17HG can attenuate neuronal apoptosis and inflammatory responses in Parkinson's disease (PD) mice [24].Recent research has linked MIR17HG to osteoarthritis [22], suggesting its involvement in other bone diseases such as osteomyelitis and osteoarthritis, where inflammation is a key factor.
To identify new diagnostic and therapeutic approaches for bone infections, this study aims to investigate the relationship between MIR17HG and molecular processes involved in inflammation during bone infections.Using cell sequencing and the Gene Expression Omnibus (GEO) database, we identified distinct Long non-coding RNA (lncRNA) linked to osteogenic differentiation and PANoptosis under TNF-α stimulation.We constructed lncRNA-miRNA-mRNA networks and performed bioinformatics analyses to explore their functions further.Furthermore, we investigated the correlation between the expression of lncRNA MIR17HG and mRNAs associated with PANoptosis in osteoblasts in response to TNF-α stimulation.This research introduces a novel framework to understand the mechanisms responsible for PANoptosis onset in osteoblasts triggered by TNF-α stimulation.

Cell culture
The murine pre-osteoblast MC3T3-E1 clonal cell line was purchased from iCell Bioscience (Shanghai, China).Cells were cultured in MEM -α with 10% FBS in a humidified environment with 5% CO 2 at 37 ℃.The medium was supplemented with penicillin, glutamine, streptomycin, and sodium pyruvate.For osteogenic differentiation, 10 mM glycerophosphate and 50 μg/ml ascorbic acid were added at the end of the logarithmic phase.

RNA isolation and sequencing
Prior to RNA isolation, MC3T3-E1 cells were cultured in six-well plates for 7 days as described.They were then divided into two groups of six independently cultured samples each: one treated with TNF-α (5 ng/ml) for 24 h and the other cultured without treatment for the same duration.RNA was isolated by scraping the cells and using Trizol reagent (ThermoFisher, Shanghai, China).Evaluation of RNA samples included: (1) agarose gel electrophoresis to analyze RNA degradation and contamination; (2) Nanodrop to determine RNA purity (OD260/280 ratio); (3) Qubit for precise RNA concentration quantification; (4) Agilent 2100 for RNA integrity assessment (Novogene, Beijing, China).The RNA samples were then sent to Beijing Novogene Technology Co., LTD for sequencing analysis.

Bibliometric approach
Bibliometric analysis is a convenient and accurate tool for discovering hot spots and trends in specific research areas [26,27].We selected Web of Science (https:// www.webof scien ce.com) as the target database and employed three search strategies: (1) TOPIC (Pyropotosis) AND TOPIC (Apoptosis) AND TOPIC (Necropotosis); (2) TOPIC (PANoptosis); (3) TOPIC (PANoptosome).We conducted keyword visualization analysis on the literature data collected from the three search strategies and specifically analyzed the literature data related to PANoptosomes using bibliometric software tools such as EXCEL 2023 and VOSviewer 1.6.19.VOSviewer 1.6.19investigates trends in specific research directions and noteworthy scientific publications, and we used it to create a keyword co-occurrence map and then cluster all keywords.We used Microsoft Office Excel 2023 to manage the data and investigate publication trends.

Screening for PANoptosis and inflammation-related lncRNA in osteomyelitis
The VennDiagram R package (version 1.7.3) was used to analyze the dataset and identify potential lncRNA candidates.The Pheatmap R package (version 1.0.12) was used to create a heatmap showing the expression of two potential lncRNAs, PANoptosis-related mRNAs, and normal osteogenic differentiation markers.Candidate lncRNAs were further characterized using the R package ggplot2 (version 3.4.1).The relationship between potential lncRNAs and PANoptosis-associated genes was examined using the R package ggplot2 (version 3.4.1).

Construction of lncRNA-miRNA-mRNA networks
DIANA Tools-lncBase V.3 (https:// diana lab.e-ce.uth.gr/ tools) was used to project miRNA targets by combining PANoptosis-related lncRNAs.We utilized the MicroRNA Target Prediction Database (miRDB) database (http:// mirdb.org/ index.html) to forecast the mRNA targets of the identified miRNAs.Pearson's correlation coefficient analysis (P < 0.05, statistically significant) was used to predict co-expressed miRNAs and mRNA targets with lncRNAs.Cytoscape 3.10.1 software was employed to construct and visualize the lncRNA-miRNA-mRNA network.

Analysis of mRNAs' functional enrichment in ceRNA networks
We performed functional enrichment analysis on a total of 2415 mRNAs that were co-expressed with lncRNAs in the lncRNA-miRNA-mRNA network.The R package clusterProfiler was used to conduct Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses.

Construction of MC3T3-E1 inflammation model
MC3T3-E1 cells were seeded into six-well plates at a density of 2 × 10 5 cells/mL.Once the cells reached 70% confluence, they were cultured in osteoblast differentiation medium for 0-14 days, and the medium (2 mL) was changed every two days.The osteoblast differentiation medium consisted of MEM-α with 10% FBS, 0.1 μg/ml dexamethasone, 10 μg/ml glycerol phosphate, and 50 μg/ ml ascorbic acid.After 14 days, cells were exposed to TNF-α (0, 5 ng/ml, 50 ng/ml) for 24 h in an incubator before further analysis.[28,29] Based on our group's previous studies, we found that the onset of PANoptosis and osteogenic differentiation inhibition was more pronounced in MC3T3-E1 cells stimulated with 50 ng/ml of TNF-α.Therefore we added this concentration to the inflammation model we constructed.

Real-time fluorescence quantitative PCR (RT-qPCR) analysis
TRIzol reagent (Vazyme, Nanjing, China) was used to extract total RNA from MC3T3-E1 cells according to the manufacturer's instructions.Total RNA was reverse transcribed into cDNA using a reverse transcription kit (Takara, Beijing, China).Real-time fluorescence quantitative PCR was performed on a LightCycler480II (Roche, Basel, Switzerland).MIR17HG and other primer sequences are listed in Supplement Table 1.Quantitative primer sequences were collected from the Primer Design Tool (https:// www.ncbi.nlm.nih.gov/ tools/ primer-blast/ index.cgi) and Primer Bank (https:// pga.mgh.harva rd.edu/ prime rbank/ index.html).

Western blotting
MC3T3-E1 cells treated with TNF-α were lysed with RIPA lysis buffer containing a protease inhibitor cocktail (Thermo Fisher Scientific, Victoria, Australia).After centrifugation at 12,000 rpm for 20 min at 4 ℃, the supernatant was collected.Protein concentration was determined using the BCA Protein Assay Kit in accordance with the manufacturer's instructions.Proteins were separated by SDS-PAGE, transferred to PVDF membranes, and blocked with 5% skimmed milk for 1 h at room temperature.Membranes were incubated with the primary antibody overnight at 4 °C, followed by a peroxidase secondary antibody for 1 h.Proteins were detected using an enhanced chemiluminescence kit (Millipore, Billerica, MA, USA) according to the manufacturer's instructions.

Statistical analysis
Data are represented as the mean ± standard deviation.Group differences were examined using Student's t-tests.Pearson's correlation coefficient was used to evaluate the relationships between variables.Data analysis was performed using R (version 4.3.1),with P < 0.05 considered statistically significant.

Identification of inflammation-related and PANoptosis-related lncRNA in osteogenic differentiation
Figure 1 shows our research workflow diagram.We built an osteoblast inflammatory model for high-throughput sequencing, allowing us to obtain expression matrices.Through this sequencing, we identified 3721 differentially expressed genes (|Log2 FC |≥ 0.75, P < 0.0001).We retrieved transcriptome profiles for normal osteogenic differentiation from the GEO database (GSE30393).We identified differentially expressed genes in GSE30393 (|Log2 FC|≥ 0.5, P < 0.05).From literature review, we identified 11 genes associated with PANoptosis: CASP1, CASP3, CASP7, CASP8, CASP9, Gasdermin E(GSDME), Gasdermin D(GSDMD), MLKL, RIPK1, RIPK3, and FADD [25].We conducted single-gene correlation analysis for these genes using |R|> 0.7 and P < 0.05 to identify PANoptosis-associated lncRNAs, based on data from high-throughput sequencing of inflammation models.MIR17HG and 4930447F24Rik were present in all three groups (Fig. 2A) and correlated with most PAN-associated genes.Heat maps showed the expression levels of MIR17HG and 4930447F24Rik in inflammation and osteogenic differentiation samples (Fig. 2B).Expression of MIR17HG and 4930447F24Rik was higher in the inflammation model than in normal osteogenically differentiated cells.The correlation heatmap showed that MIR17HG and 4930447F24Rik were associated with most panoptosis-related mRNAs (Fig. 2C).
In correlation analyses, MIR17HG was associated with at least nine mRNAs, whereas 4930447F24Rik was associated with five mRNAs (Table 1).

The lncRNA-miRNA-mRNA network construction
To investigate the function of candidate lncRNAs in osteogenic differentiation under PANoptosis and inflammation conditions, we created an lncRNA-miRNA-mRNA network (Fig. 3).Using DIANA LncBase v.3 database, we identified miRNA targets interacting with potential lncRNA targets.We evaluated mRNA targets of miRNAs (Target Score ≥ 80) using the miRDB database v6.0.We identified 18 miRNAs and 263 mRNAs centered on candidate lncRNAs (Table 2) and the lncRNA-miRNA-mRNA network visualisation is shown in Fig. 3 (Cytoscape 3.10.1).The ceRNA map showed that MIR17HG is centrally positioned.

The functional enrichment analysis of mRNAs associated with MIR17HG.
The strong correlation of MIR17HG with most PANoptosis-related genes is shown in Table 1.Given the relatively weaker correlation between 4930447F24Rik and PANoptosis-related genes, and MIR17HG's central position in the ceRNA map, we performed functional enrichment analysis on MIR17HG-associated mRNAs.GO and KEGG analyses were conducted on 2415 lncRNA-associated mRNAs (Target Score ≥ 60) to investigate biological activities and signaling pathways of potential lncRNA (Fig. 4).The GO analysis covered biological processes (BP), cellular components (CC), and molecular functions (MF).BP included stem cell differentiation, osteoblast differentiation, apoptosis, and receptor signaling via STAT.CC included neuronto-neuron synapse, distal axon, asymmetric synapse, site of polarized growth, and postsynaptic density.MF included protein serine/threonine kinase activity, SMAD binding, DNA-binding transcription repressor activity, and ubiquitinprotein transferase activity.KEGG analysis showed associations with MAPK, PI3K-Akt, TNF, JAK-STAT signaling pathways, autophagy, and osteoclast differentiation.GO and KEGG analysis results are in Supplementary Tables 2 and  3 (P < 0.05).

The role of MIR17HG and miR-17-92 cluster in PANoptosis
To verify the correlation between MIR17HG expression and PANoptosis, MC3T3-E1 cells were treated with TNF-α

TNF-α stimulation's effect on osteogenic differentiation and inflammation
After treatment with TNF-α, RT-PCR was used to detect RNA expression of key genes related to osteogenic differentiation and inflammation.As shown in Fig. 8, TNF-α treatment significantly decreased the expression of osteogenic differentiation-related genes in MC3T3-E1 cells, while the expression of inflammation-related genes increased with TNF-α concentration.These results indicate that TNF-α simulation impairs osteogenic differentiation and enhances the inflammatory environment in MC3T3-E1 cells.
PANoptosis is a newly defined form of cell death relevant to immune response-related diseases.It is regulated by the multimeric protein complex PANoptosome [43], which influences pyroptosis, apoptosis, and necroptosis.Each of these processes has been studied separately by many researchers.Studies on PANoptosis suggest that CASP1, which drives pyroptosis, CASP8, which drives apoptosis, and RIPK1 and RIPK3, which drive necroptosis, can form a PANoptosome with other components [19].This is consistent with our bibliometric data, where keyword visualization analyses showed high frequencies of CASP1, CASP8, RIPK1, and RIPK3 (Table 3).Our bibliometric analysis also shows that the NLRP3 inflammasome is mentioned in the context of pyroptosis, apoptosis, and necroptosis, acting as a sensor to regulate these cell death modalities.Pyroptosis is mediated by gasdermin family members via inflammasome-mediated caspase-1 cleavage of gasdermin D (GSDMD), caspase-11/4/5-or caspase-8-mediated cleavage of GSDMD, or caspase-3-mediated cleavage of gasdermin E (GSDME) [44][45][46][47].Apoptosis is induced by caspase-3 and -7 following\activation of upstream initiator caspases caspase-8/10 or -9 [48,49].Necroptosis is induced by RIPK3-mediated MLKL oligomerization [50][51][52].Our experimental data show that the expression of NLRP3 and genes associated with these cell death types increased in response to TNF-α stimulation.Therefore, we hypothesized that the NLRP3 inflammasome serves as an upstream signal regulating the onset of osteoblast apoptosis.Bioinformatics evidence alone is insufficient to prove this hypothesis, therefore, we detected the expression of genes related to PANoptosis and the osteogenic differentiationrelated gene ELP2 using RT-PCR and WB experiments.The results showed that MIR17HG expression was positively correlated with STAT3, ELP2, and PANoptosis-related genes, indicating that MIR17HG may directly or indirectly affect osteoblast differentiation and PANoptosis.We hypothesized that MIR17HG regulates PANoptosis during osteogenic differentiation.Upon TNF-α stimulation, MIR17HG expression increases, promoting miRNA transcription that activates and phosphorylates STAT3.MIR17HG may influence the onset of PANoptosis in osteoblasts during osteogenic differentiation by regulating STAT3 expression (Fig. 9).Furthermore, our study provides new insights into the mechanism of osteoblast apoptosis and innovative ideas for treating inflammation in bone infections and osteomyelitis.In summary, our study offers directions for further exploration of the underlying mechanisms of inflammation in bone infections.
However, our study has limitations.First, while we performed the construction and high-throughput sequencing of the inflammatory model, the sequencing data for normal osteogenic differentiated cells were obtained from the web-based GEO database.Second, we studied only one cell line, MC3T3-E1, which requires further validation in other cell lines.Third, our study only investigated the correlation  between MIR17HG and the inhibition of osteogenic differentiation and PANoptosis in osteoblasts without investigating the specific mechanism of action; these mechanisms can be further developed and refined in future studies.

Conclusion
In conclusion, our comprehensive bioinformatics, bibliometric, and several experimental validation studies have shown that that lncRNA MIR17HG is interrelated with PANoptosis and acts a pivotal part in osteoblast apoptosis under TNF-α stimulation by modulating inflammatory response pathways.These finding positions MIR17HG as a promising candidate for identifying inflammatory biomarkers and developing therapeutic targets in osteoarthritis, bone infections, and other bone-related inflammatory diseases.These results highlight the potential of MIR17HG in advancing our comprehension of the molecular processes involved in osteoblast differentiation and apoptosis in inflammatory environments.Future studies should focus on validating these findings in other cell lines and in vivo models to further elucidate the specific mechanism of MIR17HG's regulatory role.Additionally, exploring the therapeutic potential of targeting MIR17HG in clinical settings could pave the way for novel treatments for inflammatory bone diseases.

Fig. 4 Fig. 5
Fig. 4 Functional and pathway enrichment analysis of mRNAs in MIR17HG-associated mRNAs (A-D).A-C shows the GO analysis of mRNAs in MIR17HG-related mRNAs, and D show the KEGG analysis of MIR17HG-related mRNAs

Table 2
lncRNAs, miRNAs and mRNAs in the ceRNA network

Table 3
The occurrences and link strength of PANoptosis-related proteins in keyword visualization analysis